深度学习在极化SAR图像分类上的应用综述  

A Survey:the Application of Deep Learning in PolSAR Image Classification

作　　者：毕海霞 魏志强[2] BI Haixia;WEI Zhiqiang(Faculty of Engineering, University of Bristol, Bristol BS15DD, United Kingdom;Xi'an Electronic Engineering Research Institute, Xi'an 710100, China)

机构地区：[1]英国布里斯托大学工程学院,布里斯托BS15DD [2]西安电子工程研究所,陕西西安710100

出　　处：《雷达科学与技术》2021年第5期539-551,557,共14页Radar Science and Technology

基　　金：国家自然科学基金(No.61806162)。

摘　　要：极化合成孔径雷达(极化SAR)是当前最先进的遥感监测技术之一。它可以全天时、全天候地进行对地观测,并提供高分辨率、具有丰富地表信息的数据。极化SAR图像分类近年来被广泛研究和应用,而蓬勃发展的深度学习技术大大加速了其进展。基于此现状,本文对深度学习在极化SAR图像分类上的应用进行了综述。综述涵盖了不同类别的深度学习算法,包括监督、无监督、半监督和主动学习算法在此任务上的应用分析。另外,本文分析当前极化SAR图像分类所面临的挑战以及未来的发展趋势。Polarimetric synthetic aperture radar(PolSAR)is one of the most advanced and important environmental monitoring techniques owing to its all-time and all-weather observation character and strong capability to offer abundant and high-resolution target information.PolSAR image classification has been extensively investigated and applied in recent years.Specifically,the booming of deep learning greatly accelerated the development of PolSAR image classification.This paper provides a survey on the application of deep learning in classifying PolSAR images,where the utilization of different categories of deep learning-based methods,including supervised,unsupervised,semisupervised and active learning approaches are reviewed.In addition,we analyze the current challenges and future trends in this research topic.

关 键 词：综述 极化SAR 图像分类 深度学习 

分 类 号：TN958[电子电信—信号与信息处理]